2023
DOI: 10.1109/temc.2023.3317917
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Modeling Electrically Long Interconnects Using Physics-Informed Delayed Gaussian Processes

Federico Garbuglia,
Torsten Reuschel,
Christian Schuster
et al.

Abstract: This work presents a machine learning technique to model wide-band scattering parameters (S-parameters) of interconnects in the frequency domain using a new Gaussian processes (GP) model. Standard GPs with a general-purpose kernel typically assume high smoothness and therefore are not suitable to model S-parameters that are highly dynamic and oscillating due to propagation delays. The new delayed Gaussian process (τ GP) model employs a physics-informed kernel consisting of periodic components, whose fundamenta… Show more

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Cited by 5 publications
(2 citation statements)
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“…In addition, we demonstrated the potential of our neural network for designing the grid ground plane by comparing it with a traditional 3D field solver. This confirms the effectiveness of neural networks in electromagnetic modeling and parameter prediction, as previously shown in other studies [29][30][31][32][33][34][35][36]. Given its predictive capability, the model represents a powerful tool for system-level simulation and optimization, eliminating the need for tedious and repetitive simulation processes and saving significant time and computational resources.…”
Section: Discussionsupporting
confidence: 86%
See 1 more Smart Citation
“…In addition, we demonstrated the potential of our neural network for designing the grid ground plane by comparing it with a traditional 3D field solver. This confirms the effectiveness of neural networks in electromagnetic modeling and parameter prediction, as previously shown in other studies [29][30][31][32][33][34][35][36]. Given its predictive capability, the model represents a powerful tool for system-level simulation and optimization, eliminating the need for tedious and repetitive simulation processes and saving significant time and computational resources.…”
Section: Discussionsupporting
confidence: 86%
“…ML models have high simulation speeds and do not need to fully understand the internal structure of modeling objects. Thus, S-parameter modeling technology based on ANNs has attracted increasing attention, and preliminary feasibility studies have been recently carried out [33][34][35][36]. While both SVMs and ANNs demonstrate efficacy in fitting, they have yet to be extensively applied in grid ground plane design parameters.…”
Section: Introductionmentioning
confidence: 99%